Fault Diagnosis in the Slip Frequency Plane of Induction Machines Working in Time-Varying Conditions

[EN] Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms-as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform-which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.This work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J.; Riera-Guasp, M. (2020). Fault Diagnosis in the Slip Frequency Plane of Induction Machines Working in Time-Varying Conditions. Sensors. 20(12):1-26. https://doi.org/10.3390/s20123398S126201

Fault detection and diagnosis of a multistage helical gearbox using magnitude and phase information from vibration signals

Vibration generated by a gearbox carries a great deal of information regarding its health condition. This research aims primarily on the detection and diagnosis of tooth defects in a multistage gearbox based on advanced vibration analysis. Time synchronised averaging (TSA) analysis is effective at removing noise but it is inefficient in implementation and in diagnosing different types of faults such as bearing defects other than gears. Conventional bispectrum (CB) can eliminate Gaussian noise while it preserves the signal’s phase information, however its overpopulated contents can still provide inaccurate information regarding to different types of gear faults. Recently developed modulation signal bispectrum (MSB) has the high potential to lead to the high accuracy of diagnostics of gearboxes as it more effectively characterises modulation signals such as gearbox vibrations. Therefore, the research takes MSB as the fundamental tool for analysing gearbox vibration signals and developing accurate diagnostic techniques. Firstly, it has realised that conventional techniques often ignore the effect of phase information in gearbox diagnostics. This thesis then focuses on developing CB and MSB based techniques for detecting and diagnosing of gearbox faults. Secondly, it has found that vibration responses from a multiple stage gearbox have high interferences between amplitude modulation (AM) and phase modulation (PM) which can be formalised from both gear faults and inherent manufacturing errors. However, the faults can induce wider bandwidth vibrations. Correspondingly, optimal component based schemes are also developed based on the use of MSB coherence results. Then the proposed MSB method allows an effective gearbox diagnosis using the signals in a narrower frequency band that is below twice the rotational frequency plus the highest meshing frequency amongst different gear transmission stages, being more suitable for wireless network condition monitoring systems. It has also found that the signals at resonance frequencies has a higher signal-to-noise ratio and more effective for obtaining accurate diagnosis. Also software encoder based TSA was found to be not robust and accurate due to the influences of noise and referencing components on obtaining a reliable phase signal for implementing TSA. Finally, the diagnostics carried out upon different fault cases using both CB and MSB have verified the proposed approaches can provide accurate diagnostic results, and with the new MSB based detector and estimator being more effective in differentiating between diffident fault locations for two local and one non-uniformly distributed tooth damages in a two stage helical gearbox

Enhanced information extraction from noisy vibration data for machinery fault detection and diagnosis

As key mechanical components, bearings and gearboxes are employed in most machines. To maintain efficient and safe operations in modern industries, their condition monitoring has received massive attention in recent years. This thesis focuses on the improvement of signal processing approaches to enhance the performance of vibration based monitoring techniques taking into account various data mechanisms and their associated periodic, impulsive, modulating, nonlinear coupling characteristics along with noise contamination. Through in-depth modelling, extensive simulations and experimental verifications upon different and combined faults that often occur in the bearings and gears of representative industrial gearbox systems, the thesis has made following main conclusions in acquiring accurate diagnostic information based on improved signal processing techniques: 1) Among a wide range of advanced approaches investigated, such as adaptive line enhancer (ALE), wavelet transforms, time synchronous averaging (TSA), Kurtogram analysis, and bispectrum representations, the modulation signal bispectrum based sideband estimator (MSB-SE) is regarded as the most powerful tool to enhance the periodic fault signatures as it has the unique property of simultaneous demodulation and noise reduction along with ease of implementation. 2) The proposed MSB-SE based robust detector can achieve optimal band selection and envelope spectrum analysis simultaneously and show more reliable results for bearing fault detection and diagnosis, compared with the popular Kurtogram analysis which highlights too much on localised impulses. 3) The proposed residual sideband analysis yields accurate and consistent diagnostic results of planetary gearboxes across wide operating conditions. This is because that the residual sidebands are much less influenced by inherent gear errors and can be enhanced by MSB analysis. 4) Combined faults in bearings and gears can be detected and separated by MSB analysis. To make the results more reliable, multiple slices of MSB-SE can be averaged to minimise redundant interferences and improve the diagnostic performance

Planetary gearbox condition monitoring based on modulation analysis

The epicycle gearbox or planetary gearbox (PG) is a central power transmission systems of important machines such as helicopters and wind turbines which are mission critical and high cost systems. Condition monitoring (CM) has been explored extensively in recent years to avoid any unexpected interruptions and severe accidences caused by faults PGs. Although, considerable advancements in CM techniques, there still existed significant deficiency such as insensitivity, false diagnosis and high costs in implementing such techniques in industries. To improve CM techniques, therefore, this thesis focuses on an investigation of advanced signal analysis techniques such as higher order spectra (HOS) in order to achieve full characterisation of the nonlinear modulation processes of PG dynamics and thereby develop accurate diagnostic techniques. The lumped mass model is established for modelling the dynamic behaviour of the PG under investigation, which allows the vibration behaviours to be understood for analysing different abnormalities such as tooth breakages and gear errors. This paves the way for subsequent data analytics and fault diagnostics using modulation signal bispectrum (MSB) that allows the vibration data to be examined through HOS, but it is significantly efficient in characterising the multiple and nonlinear modulations of PG dynamics alongside superior noise reduction performance. Different degrees of misalignments in the PG drive system has been investigated and successfully diagnosed using MSB analysis of vibration measurements.. Moreover, the investigation included detection of tooth breakage faults of different severities in both the sun and a planet gear. The tooth faults were diagnosed using the recently developed MSB through accurately representation and estimate of residual sidebands induced by these faults. Consequently, MSB analysis produces an accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for wide operating conditions. Furthermore, these fault diagnosis practices allows the establishment of residual sideband analysis approach. These residual sidebands resulting from the out-of-phase superposition of vibration waves due to asymmetric, multiple meshing sources are much less influenced by gear errors than the in-phase sidebands due to faults or new occurrences of the symmetricity. MSB can provide an accurate characterisation of the residual sidebands and consequently produces consistent diagnosis as confirmed by both simulation and experiment

INVESTIGATION OF DYNAMIC RESPONSES OF ON-ROTOR WIRELESS SENSORS FOR CONDITION MONITORING OF ROTATING MACHINES

The most common sensors that are used to monitor the condition of a machine health are wired accelerometers. The big advantages of using these types of accelerometers are their high performance and good stability. However, they have certain drawbacks as well. These accelerometers are large in size and require a cable for external power source. Hence a more reliable and cheaper alternatives of these conventional accelerometers are needed that can eliminate the drawbacks of the wired accelerometers. This thesis reports the application of wireless Micro-Electro-Mechanical System (MEMS) accelerometer for machinery condition monitoring. These sensors are so small that they can be easily mounted on the rotating machine parts and can acquire dynamic information very accurately. One critical problem in using an on-rotor accelerometer is to extract the true tangential acceleration from the MEMS outputs. In this research, the mathematical model of an on-rotor triaxial MEMS accelerometer output signals is studied, and methods to eliminate the gravitational effect projected on X-axis (tangential direction) are proposed. The true tangential acceleration that correlates to the instantaneous angular speed (IAS) is reconstructed by combining two orthogonal outputs from the sensor that also contain gravitational accelerations. To provide more accurate dynamic characteristics of the rotating machine and hence achieving high-performance monitoring, a tiny MEMS accelerometer (AX3 data logger) has been used to obtain the on-rotor acceleration data for monitoring a two-stage reciprocating compressor (RC) based on the reconstruction of instantaneous angular speed (IAS). The findings from the experiments show that the conditions of the RC can be monitored and different faults can be identified using only one on-rotor MEMS accelerometer installed on compressor’ flywheel. In addition, the data collection method is improved by considering the wireless data transmission technique which enables online condition monitoring of the compressor. Thus, a wireless MEMS accelerometer node is mounted on the RC to measure the on-rotor acceleration signals. The node allows the measured acceleration data to be streamed to a remote host computer via Bluetooth Low Energy (BLE) module. In addition, the device is miniaturised so that can be conveniently mounted on a rotating rotor and can be driven by a battery powered microcontroller. To benchmark the wireless sensor performance, an incremental optical encoder was installed on the compressor flywheel to acquire the instantaneous angular speed (IAS) signal. Furthermore, conventional accelerometer mounted on the machine’s housing provide lower accuracy in diagnosis the faults for planetary gearboxes because of the planet gears’ varying mesh excitation due to its carrier movement. In contrast, installation of the smaller AX3 MEMS accelerometers is done at diametrically opposite direction to the each other of the planetary gearbox’s low-speed input shaft, allowing measurement of the acceleration signals which are used for condition monitoring of the gearbox. The findings from the experiments demonstrate that when tangential acceleration is measured at the planetary gearbox’s low-speed input shaft, effective fault identification is possible, offering reliability and economy in monitoring the health of planetary gearboxes

PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

THE ANALYSIS OF POWER SUPPLY SIGNALS BY INCLUDING PHASE EFFECTS FOR MACHINE FAULT DIAGNOSIS

Substantial efforts have been devoted to developing Condition Monitoring techniques to provide timely preventative measures for ensuring a safe and cost-effective operation of electromechanical systems. High investment of installation and implementation in using conventional condition monitoring techniques such as vibration based monitoring makes it difficult to be used in most industries such as petrochemical processing, food and drinking processes, paper mills and so on where large number of motor drives are used but operational profits are very limited. To overcome the shortages of vibration based monitoring, this project focuses on developing condition monitoring techniques based on electrical signal analysis which can offer great savings as electric signatures that can monitor a large system are generally available in most motor drives. However, fault signatures in electrical signatures such as instantaneous current and voltage signals are very weak and contaminated by noise. To enhance the signatures, this study has focused on using two more advanced signal processing approaches: 1) Modulation signal bispectrum analysis, which enhances the modulation and suppresses random noise by including phase linkages. 2) Instantaneous phase quantities including conventional instantaneous power factor and a novel instantaneous phase of voltage and current which highlights instantaneous phase changes through a summation of instantaneous phases in current and voltage signals. It has the ability of enhancing the phase components that are of the same phases in both voltage and current signals, and also cancel out any random components to a great extent, producing more diagnostic information. These two approaches emphasis the use of phase information along with that of amplitudes and frequency in a signal that is based on in most previous methods in the condition monitoring fields. Based on a general electromechanical system comprising of a AC motor, a gearbox and a DC generator, it firstly explored the characteristics of the signatures by modelling and simulation studies, which lead to that faults in a sensorless Variable speed drive system can produce combined amplitude and frequency modulation effects in both current and voltage signals fed to the AC motor. Moreover, the modulating frequencies and levels are closely associated with the rotational frequencies of the gearbox and fault severity respectively, which become more significant at higher load conditions. Experimental evaluations have found that these two proposed methods allow common faults in the downstream gearbox including gear tooth breakage, oil shortage and excessive bearing clearances to be detected and diagnosed under high load conditions, showing the effectiveness and accuracy of these two new approaches. Furthermore, the results show that the electrical signature analysis is capable of detecting and diagnosing different faults in sensorless variable speed drive systems. Instantaneous phase of voltage and current has been shown to provide more consistent and accurate separation between the three different faults under different loads. The use of the modulation signal bispectrum analysis succeed to provide an improved, accurate and reliable diagnostic with the power signal providing the best means of detecting and determining fault severity with good separation between fault levels